Substance Use Disorders in the U.S. Armed Forces

O’Brien, Charles P.; Oster, Maryjo; Morden, Emily

doi:10.17226/13441

Cited by 21 publications

(2 citation statements)

References 221 publications

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“…The Air Force Alcohol and Drug Abuse Prevention and Treatment (ADAPT) Program [12] and the Army Substance Abuse Program (ASAP) [13] include drug prevention as an integral duty within the scope of their missions, as directed by the Department of Defense [14]. The results of this study could assist both the ADAPT Program and the ASAP in their prevention goals by providing an innovative selective prevention process to discover individuals at greater risk for substance use disorders [15]. Although a continuing topic of research and debate, Williams argues that THC use may also increase the risk for more serious opioid use disorder [16].…”

Section: Introductionmentioning

confidence: 99%

Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use

Zoboroski

Wagner

Langhals

2021

IJERPH

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Marijuana is the most commonly abused drug for military personnel tested at the Air Force Drug Testing Laboratory. A publicly available dataset of drug use, personality trait scores and demographic data was modeled with logistic regression, decision tree and neural network models to determine the extent to which marijuana use can be predicted using personality traits. While the logistic regression model had lower performance than the neural network model, it matched the sensitivity of prior work (0.80), achieved a high level of significance (p < 0.05) and yielded valuable inferences. It implied that younger, less educated individuals who exhibit sensation-seeking behavior and are open to experience tend to be at higher risk for THC use. A method for performing an iterative multidimensional neural network hyperparameter search is presented, and two iterations of a 6-dimensional search were performed. Metrics were used to select a family of 8 promising models from a cohort of 4600 models, and the best NN model’s 0.87 sensitivity improved upon the literature. The model met an f1 overfitting threshold on the test and holdout datasets, and an accuracy sensitivity analysis on a holdout-equivalent dataset yielded a 95% CI of 0.86 ± 0.04. These results have the potential to increase the efficacy of drug prevention and intervention programs.

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Section: Introductionmentioning

confidence: 99%

Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use

Zoboroski

Wagner

Langhals

2021

IJERPH

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“…Substance use problems are common among post-9/11 veterans who use VHA care. [8][9][10] At-risk substance use behaviors may have started during military service where binge drinking has been normative for decades, 11,12 and prescribing opioids for pain and injuries was routine during the Afghanistan/Iraq conflicts, particularly during the first decade. [13][14][15][16] Studies have found that military members with TBI and post-9/11 deployment history had increased risk for postdeployment alcohol misuse, 17 binge drinking and heavy drinking, 18,19 and receipt of long-term opioid therapy.…”

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confidence: 99%

Risk of Adverse Outcomes Among Veterans Who Screen Positive for Traumatic Brain Injury in the Veterans Health Administration But Do Not Complete a Comprehensive Evaluation: A LIMBIC-CENC Study

Pogoda

Adams

Carlson

et al. 2023

Journal of Head Trauma Rehabilitation

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Objective: To examine whether post-9/11 veterans who screened positive for mild traumatic brain injury (mTBI) but did not complete a Comprehensive TBI Evaluation (CTBIE) were at higher risk of subsequent adverse events compared with veterans who screened positive and completed a CTBIE. Upon CTBIE completion, information assessed by a trained TBI clinician indicates whether there is mTBI history (mTBI+) or not (mTBI−). Setting: Veterans Health Administration (VHA) outpatient services. Participants: A total of 52 700 post-9/11 veterans who screened positive for TBI were included. The follow-up review period was between fiscal years 2008 and 2019. The 3 groups studied based on CTBIE completion and mTBI status were: (1) mTBI+ (48.6%), (2) mTBI− (17.8%), and (3) no CTBIE (33.7%). Design: This was a retrospective cohort study. Log binomial and Poisson regression models adjusting for demographic, military, pre-TBI screening health, and VHA covariates examined risk ratios of

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History and Current Framework of Social Work Services Delivered to Families and Communities Within the US Military

Robichaux

Franklin²

2022

Military and Veterans Studies

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Substance Use Disorders in the U.S. Armed Forces

Cited by 21 publications

References 221 publications

Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use

Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use

Risk of Adverse Outcomes Among Veterans Who Screen Positive for Traumatic Brain Injury in the Veterans Health Administration But Do Not Complete a Comprehensive Evaluation: A LIMBIC-CENC Study

History and Current Framework of Social Work Services Delivered to Families and Communities Within the US Military

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